Severe aortic stenosis management in heart valve centres compared with primary/secondary care centres
Bibliographic record
Abstract
OBJECTIVE: Current guidelines recommend use of heart valve centres (HVCs) to deliver optimal quality of care for patients with valve disease but there is no evidence to support this. The hypothesis of this study is that patient care with severe aortic stenosis (AS) will differ in HVCs compared with satellite centres. We aimed to compare the treatment of patients with AS at HVCs (tertiary care hospitals with full access to AS interventions) to satellites (hospitals without such access). METHODS: is a European, observational, prospective registry enrolling consecutive patients with newly diagnosed severe AS at four HVCs and 10 satellites. Clinical characteristics, interventions performed and outcomes up to 1 year by site-type were examined. RESULTS: Among 790 patients, 594 were recruited in HVCs and 196 in satellites. At baseline, patients in HVCs had more severe valve disease (higher peak aortic velocity (4.3 vs 4.1 m/s; p=0.008)) and greater comorbidity (coronary artery disease (CAD) (44% vs 27%; p<0.001) prior myocardial infarction (MI) (11% vs 5.1%; p=0.011) and chronic pulmonary disease (17% vs 8.9%; p=0.007)) than those presenting in satellites. An aortic valve replacement was performed more often by month 3 in HVCs than satellites in the overall population (52.6% of vs 31.3%; p<0.001) and in symptomatic patients (66.7% vs 43.2%, p<0.001). One-year survival rate was higher for patients in HVCs than satellites (HR2.19; 95% CI 1.28 to 3.73 total population and 2.89 (95%CI 1.64 to 5.11) for symptomatic patients. CONCLUSIONS: Our data support the implementation of referral pathways that direct patients to HVCs performing both surgery and transcatheter interventions. TRIAL REGISTRATION NUMBER: NCT03112629.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".